Priority is claimed to Korean Patent Application No. 10-2004-0007231, filed on Feb. 4, 2004, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference.
1. Field of the Invention
The present invention relates to a method and apparatus for determining a geomagnetic field and a method and apparatus for determining an azimuth angle of a moving object using the same, and more particularly, to a method and apparatus for determining a geomagnetic field by using a compass and a method and apparatus for determining an azimuth angle of a moving object using the same.
2. Description of the Related Art
Pose estimation is necessary to an intelligent moving object such as a robotic vehicle. The pose estimation is performed by using absolute sensors or relative sensors. As shown in FIG. 1, the pose estimation of the object moving on a plane is to estimate a position (x, y) and an orientation angle θ of the object. That is, the term “pose” denotes position and orientation. The pose estimation of a stationary object is a specific one of the pose estimations of a moving object. Therefore, a stationary object is considered to be a moving object without loss of generality.
Examples of the absolute sensors for detecting an absolute position or pose of an object such as a robot include a camera, a laser scanner, a sonar sensor, a global positioning system (GPS), and a compass. On the other hand, examples of the relative sensors for measuring variations of a relative position of an object and integrating the variations to estimate a position or pose include a gyro, an accelerometer, and an odometer, which is a kind of an encoder provided to a motor.
These sensors have their own disadvantage as well as advantages. The camera is so sensitive to ambient brightness that inaccurate data may be obtained. The laser scanner can obtain somewhat accurate data, but its price is very high. In addition, in a case where there are a lot of obstacles, the laser scanner is not useful. The sonar has a low accuracy. The GPS can be used only outdoors, and its accuracy is low. On the other hand, the relative sensors are essentially involved in an integration error because the variations obtained by the relative sensors need to be integrated. In addition, the gyro and accelerometer have their own drift errors. Therefore, in order to compensate for disadvantages of the sensors, sensor fusion of multiple sensors has been developed and used for the pose estimation of the moving object.
On the other hand, the compass is a sensor for indicating a magnetic north at any place on the earth. Therefore, an azimuth angle of the object can stably be obtained with the compass. Furthermore, by using sensor fusion of the compass with other sensors, pose estimation of the moving object such as the robot can be more stably performed. However, since there is generally external magnetic field disturbances at home and office, the azimuth angle detected by the compass may be distorted. Therefore, approaches to coping with the distorted azimuth angle have been needed.
Various approaches to coping with the distorted azimuth angle of the compass due to the external magnetic field disturbance have been proposed. For example, there are methods of modeling a compass to obtain an azimuth angle of the compass. However, validities of the methods have not been proved with any experiments. An example of the method is disclosed in an article by Wang Hung-de Deng, titled “Research on High Precision Intelligent Digital Magnetic Heading System,” at Proceedings of the IEEE International Conference on Industrial Technology (1994), p 734–737, 1994.
In addition, there are methods using sensor fusion of a compass with other sensors. However, in these methods, the distortion of the azimuth angle of the compass due to an external magnetic field disturbance is not considered. An example of the method using the sensor fusion is disclosed in an article by Roumeliotis S. I., Sukhatme G. S., and Bekey, G. A, titled “Circumventing Dynamic Modeling: Evaluation of the Error-state Kalman Filter Applied to Mobile Robot Localization,” at Proceedings of IEEE International Conference on Robotics and Automation (1994), p 1656–1663, 1999.